Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty, Samhitha; Rupa, Ch.; Ch, Anusha; Iwendi, Celestine; Gadekallu, Thippa Reddy

doi:10.3390/w14050707

Cited by 49 publications

(21 citation statements)

References 33 publications

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“…Given the enormous input data dimensionality and little labeled data, direct application of supervised (shallow or deep) convolutional networks to multi-and [28] hyperspectral imaging is problematic. The recommend combining unsupervised learning [29] of sparse features with greedy layer-wise unsupervised pretraining. [30] The technique uses sparse representations to enforce population and lifetime sparsity and to compute the logarithm of every pixel using data normalization.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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A significant amount of effort and cost is required to collect training samples for remote sensing image classifications. The study of remote sensing and how to read multispectral images is becoming more important. High-dimensional multispectral images are created by the various bands that show how materials behave. The need for more information about things and the improvement of sensor resolutions have led to the creation of multispectral data with a higher size. In recent years, it has been shown that the high dimensionality of these data makes it hard to preprocess them in multiple ways. Recent research has demonstrated that one of the most crucial methods to address this issue is by adopting a variety of learning strategies. But as the data gets more complicated, these methodologies are not adequate to support. The proposed methodology shows that the classification experiment using remote sensing images indicates the maximum likelihood classifier with different deep learning models; weight vector (WV) AdaBoost and ADAM can greatly limit overfitting, and it obtains high classification accuracy. Proposed VGG16 and Inception v3 increase classification accuracy along with optimization process produce 96.08%.

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Section: Related Workmentioning

confidence: 99%

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

et al. 2022

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“…The major objective of the proposed work is to overcome the disadvantages that exists in the prediction model using handwritten statements where multiple formats are not present is any existing models ( 1 – 29 ). In addition, only occasional treatments are provided for predicting depression with low complexity algorithms.…”

Section: Literature Surveymentioning

confidence: 99%

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Shitharth

et al. 2022

Front. Public Health

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The majority of the current-generation individuals all around the world are dealing with a variety of health-related issues. The most common cause of health problems has been found as depression, which is caused by intellectual difficulties. However, most people are unable to recognize such occurrences in them, and no procedures for discriminating them from normal people have been created so far. Even some advanced technologies do not support distinct classes of individuals as language writing skills vary greatly across numerous places, making the central operations cumbersome. As a result, the primary goal of the proposed research is to create a unique model that can detect a variety of diseases in humans, thereby averting a high level of depression. A machine learning method known as the Convolutional Neural Network (CNN) model has been included into this evolutionary process for extracting numerous features in three distinct units. The CNN also detects early-stage problems since it accepts input in the form of writing and sketching, both of which are turned to images. Furthermore, with this sort of image emotion analysis, ordinary reactions may be easily differentiated, resulting in more accurate prediction results. The characteristics such as reference line, tilt, length, edge, constraint, alignment, separation, and sectors are analyzed to test the usefulness of CNN for recognizing abnormalities, and the extracted features provide an enhanced value of around 74%higher than the conventional models.

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“…Gadekallu et al [16] proposed an approach to detect the ships from remote sensing satellite imagery. This is based on a deep learning approach, namely You only look once version 3 (YOLOV3).…”

Section: Literature Surveymentioning

confidence: 99%

“…T po T po + F Neg (16) Figure 13 shows the difference between predicted image threshold to the default threshold.…”

Section: R C =mentioning

confidence: 99%

ECDSA-Based Water Bodies Prediction from Satellite Images with UNet

Rupa

Gadamsetty

et al. 2022

Water

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The detection of water bodies from satellite images plays a vital role in research development. It has a wide range of applications such as the prediction of natural disasters and detecting drought and flood conditions. There were few existing applications that focused on detecting water bodies that are becoming extinct in nature. The dataset to train this deep learning model is taken from Kaggle. It has two classes, namely water bodies and masks. There is a total of 2841 sentinel-2 satellite images with corresponding 2841 masks. Additionally, the present work focuses on using UNet, Tensorflow to detect the water bodies. It uses a Nadam optimizer to reduce the losses. It also finds best-optimized parameters for the activation function, a number of nodes in each layer. This proposed model achieves integrity by embedding a security feature Elliptic Curve Digital Signature Algorithm (ECDSA). It generates a digital signature for the predicted area of water bodies which helps to secure the key and the detected water bodies while transmitting in a channel. Thus, the proposed model ensures the performance accuracy of 94% which can also work the same for edge detection, detection in blurred and low-resolution images. The model is highly robust.

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Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Cited by 49 publications

References 33 publications

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

An Efficient Multispectral Image Classification and Optimization Using Remote Sensing Data

Perception Exploration on Robustness Syndromes With Pre-processing Entities Using Machine Learning Algorithm

ECDSA-Based Water Bodies Prediction from Satellite Images with UNet

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